{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "991fb6df-1e2d-457c-af77-ada145632bca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "共生成行数： 1200\n",
      "\n",
      "按区域×年份汇总的销量：\n",
      "year    2021  2022\n",
      "region            \n",
      "华东      1003  1215\n",
      "西北      1265  1321\n",
      "东北      1531  1426\n",
      "华北      1436  1531\n",
      "华南      2066  2238\n",
      "\n",
      "模拟 Excel 数据表：\n",
      "   区域   占位1  2022年销量   占位2  2021年销量\n",
      "0  华东  2238     1215  1215     1003\n",
      "1  西北  2132     1321  1215     1265\n",
      "2  东北  2027     1426  1215     1531\n",
      "3  华北  1922     1531  1215     1436\n",
      "4  华南  1215     2238  1215     2066\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1820x585 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "需要的第三方库：\n",
    "    pip install faker pandas matplotlib\n",
    "\"\"\"\n",
    "\n",
    "import random\n",
    "from datetime import datetime\n",
    "from faker import Faker\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fake = Faker(\"zh_CN\")\n",
    "random.seed(42)\n",
    "Faker.seed(42)\n",
    "\n",
    "# ===================== 1. 配置区域和目标销量 =====================\n",
    "\n",
    "# 目标：保证按【区域、年份】汇总后的 quantity 等于下表：\n",
    "# 区域   2022年销量   2021年销量\n",
    "# 华东   1215        1003\n",
    "# 西北   1321        1265\n",
    "# 东北   1426        1531\n",
    "# 华北   1531        1436\n",
    "# 华南   2238        2066\n",
    "\n",
    "target_sales_2022 = {\n",
    "    \"华东\": 1215,\n",
    "    \"西北\": 1321,\n",
    "    \"东北\": 1426,\n",
    "    \"华北\": 1531,\n",
    "    \"华南\": 2238,\n",
    "}\n",
    "\n",
    "target_sales_2021 = {\n",
    "    \"华东\": 1003,\n",
    "    \"西北\": 1265,\n",
    "    \"东北\": 1531,\n",
    "    \"华北\": 1436,\n",
    "    \"华南\": 2066,\n",
    "}\n",
    "\n",
    "regions = list(target_sales_2022.keys())\n",
    "\n",
    "ROWS_PER_REGION_YEAR = 120   # 每个 区域-年份 生成 120 行 => 共 5*2*120 = 1200 行 ≥ 1000\n",
    "\n",
    "\n",
    "def random_date_in_half_year(year: int):\n",
    "    \"\"\"给定年份，生成该年上半年（1-6月）中的任意时间\"\"\"\n",
    "    month = random.randint(1, 6)\n",
    "    day = random.randint(1, 28)  # 简化避免月底问题\n",
    "    hour = random.randint(0, 23)\n",
    "    minute = random.randint(0, 59)\n",
    "    second = random.randint(0, 59)\n",
    "    return datetime(year, month, day, hour, minute, second)\n",
    "\n",
    "\n",
    "def random_quantity_split(total: int, n: int):\n",
    "    \"\"\"\n",
    "    把 total 拆成 n 份随机的正整数，保证 sum(result) == total\n",
    "    思路：每份先分配 1，剩余随机撒到各行\n",
    "    \"\"\"\n",
    "    if total < n:\n",
    "        raise ValueError(\"total 必须 ≥ n\")\n",
    "    quantities = [1] * n\n",
    "    remaining = total - n\n",
    "    while remaining > 0:\n",
    "        idx = random.randint(0, n - 1)\n",
    "        add = random.randint(1, min(10, remaining))  # 每次最多加 10，增加随机性\n",
    "        quantities[idx] += add\n",
    "        remaining -= add\n",
    "    random.shuffle(quantities)\n",
    "    return quantities\n",
    "\n",
    "\n",
    "# ===================== 2. 生成模拟 erp_order 数据 =====================\n",
    "\n",
    "rows = []\n",
    "auto_id = 1\n",
    "\n",
    "for region in regions:\n",
    "    for year, target_dict in [(2021, target_sales_2021), (2022, target_sales_2022)]:\n",
    "        target_total_qty = target_dict[region]\n",
    "        qty_list = random_quantity_split(target_total_qty, ROWS_PER_REGION_YEAR)\n",
    "\n",
    "        for q in qty_list:\n",
    "            order_time = random_date_in_half_year(year)\n",
    "            payment_date = order_time\n",
    "            shipping_date = order_time\n",
    "\n",
    "            unit_price = round(random.uniform(80, 400), 2)     # 商品单价\n",
    "            product_amount = round(unit_price * q, 2)\n",
    "            payable_amount = product_amount\n",
    "            paid_amount = product_amount\n",
    "\n",
    "            internal_order_number = f\"IN{year}{fake.random_number(digits=8, fix_len=True)}\"\n",
    "            online_order_number = f\"ON{year}{fake.random_number(digits=8, fix_len=True)}\"\n",
    "            sub_order_number = f\"S{fake.random_number(digits=6, fix_len=True)}\"\n",
    "            online_sub_order_number = f\"OS{fake.random_number(digits=6, fix_len=True)}\"\n",
    "            original_online_order_number = online_order_number  # 简化：视为相同\n",
    "\n",
    "            spu = f\"SPU{fake.random_number(digits=6, fix_len=True)}\"\n",
    "            sku = f\"{spu}-{random.choice(['S', 'M', 'L', 'XL'])}\"\n",
    "\n",
    "            row = {\n",
    "                \"id\": auto_id,\n",
    "                \"internal_order_number\": internal_order_number,\n",
    "                \"online_order_number\": online_order_number,\n",
    "                \"store_name\": random.choice([\"旗舰店\", \"自营店\", \"特卖店\"]),\n",
    "                \"full_channel_user_id\": fake.uuid4().replace(\"-\", \"\")[:32],\n",
    "                \"shipping_date\": shipping_date,\n",
    "                \"payment_date\": payment_date,\n",
    "                \"payable_amount\": payable_amount,\n",
    "                \"paid_amount\": paid_amount,\n",
    "                \"status\": random.choice([\"已发货\", \"已完成\", \"待收货\"]),\n",
    "                \"consignee\": fake.name(),\n",
    "                \"spu\": spu,\n",
    "                \"order_time\": order_time,\n",
    "                # 这里把区域直接放到 province 字段中，方便后面按照“区域”汇总\n",
    "                \"province\": region,\n",
    "                \"city\": fake.city_name(),\n",
    "                \"platform\": random.choice([\"天猫\", \"淘宝\", \"京东\", \"抖音\"]),\n",
    "                \"sub_order_number\": sub_order_number,\n",
    "                \"online_sub_order_number\": online_sub_order_number,\n",
    "                \"original_online_order_number\": original_online_order_number,\n",
    "                \"sku\": sku,\n",
    "                \"quantity\": q,\n",
    "                \"unit_price\": unit_price,\n",
    "                \"product_name\": random.choice([\"连衣裙\", \"针织衫\", \"T恤\", \"外套\", \"裤装\"]),\n",
    "                \"color_and_spec\": random.choice([\"红色 / M\", \"黑色 / L\", \"白色 / S\", \"蓝色 / XL\"]),\n",
    "                \"product_amount\": product_amount,\n",
    "                \"original_price\": round(unit_price * random.uniform(1.0, 1.5), 2),\n",
    "                \"is_gift\": random.choice([\"否\", \"是\"]),\n",
    "                \"sub_order_status\": random.choice([\"正常\", \"已取消\", \"已关闭\"]),\n",
    "                \"refund_status\": random.choice([\"未申请退款\", \"成功退款\", \"退款中\"]),\n",
    "                \"registered_quantity\": q,\n",
    "                \"actual_refund_quantity\": 0,\n",
    "            }\n",
    "            rows.append(row)\n",
    "            auto_id += 1\n",
    "\n",
    "# 转为 DataFrame\n",
    "df_orders = pd.DataFrame(rows)\n",
    "\n",
    "print(\"共生成行数：\", len(df_orders))\n",
    "\n",
    "# ===================== 3. 校验汇总结果是否与目标表一致 =====================\n",
    "\n",
    "df_orders[\"year\"] = df_orders[\"shipping_date\"].dt.year\n",
    "df_orders.rename(columns={\"province\": \"region\"}, inplace=True)\n",
    "\n",
    "summary = (\n",
    "    df_orders.groupby([\"region\", \"year\"])[\"quantity\"]\n",
    "    .sum()\n",
    "    .unstack()\n",
    "    .astype(int)\n",
    "    .reindex(regions)\n",
    ")\n",
    "print(\"\\n按区域×年份汇总的销量：\")\n",
    "print(summary)\n",
    "\n",
    "# 你给的表格形式（区域, 占位1, 2022年销量, 占位2, 2021年销量）\n",
    "excel_like_df = pd.DataFrame({\n",
    "    \"区域\": regions,\n",
    "    \"占位1\": [2238, 2132, 2027, 1922, 1215],   # 占位列，不用于计算，只是模仿你Excel的结构\n",
    "    \"2022年销量\": [target_sales_2022[r] for r in regions],\n",
    "    \"占位2\": [1215] * len(regions),\n",
    "    \"2021年销量\": [target_sales_2021[r] for r in regions],\n",
    "})\n",
    "print(\"\\n模拟 Excel 数据表：\")\n",
    "print(excel_like_df)\n",
    "\n",
    "# ===================== 4. 使用 Matplotlib 画和模板相似的对称条形图（排版优化版） =====================\n",
    "\n",
    "# 为了和原图顺序一致，按 2022 年销量从小到大排列（华东→华北→东北→西北→华南）\n",
    "plot_order = (\n",
    "    excel_like_df.sort_values(\"2022年销量\", ascending=True)\n",
    "    .reset_index(drop=True)\n",
    ")\n",
    "areas = plot_order[\"区域\"].tolist()\n",
    "sales_2022 = plot_order[\"2022年销量\"].values\n",
    "sales_2021 = plot_order[\"2021年销量\"].values\n",
    "\n",
    "# 左右对称：左侧用负值表示 2022，右侧用正值表示 2021\n",
    "values_2022 = -sales_2022\n",
    "values_2021 = sales_2021\n",
    "\n",
    "y_pos = np.arange(len(areas))\n",
    "\n",
    "plt.rcParams[\"font.sans-serif\"] = [\"SimHei\"]   # 中文\n",
    "plt.rcParams[\"axes.unicode_minus\"] = False     # 负号\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(14, 4.5), dpi=130)\n",
    "fig.patch.set_facecolor(\"#111633\")\n",
    "ax.set_facecolor(\"#111633\")\n",
    "\n",
    "# 计算对称的坐标范围\n",
    "max_val = max(sales_2022.max(), sales_2021.max())\n",
    "ax.set_xlim(-max_val * 1.4, max_val * 1.4)\n",
    "\n",
    "# 画左右两侧条形（高度一致，y 相同，视觉更整齐）\n",
    "bar_height = 0.45\n",
    "ax.barh(y_pos, values_2022, color=\"#007ACC\", height=bar_height)\n",
    "ax.barh(y_pos, values_2021, color=\"#FF4E73\", height=bar_height)\n",
    "\n",
    "# y 轴显示区域名称\n",
    "ax.set_yticks(y_pos)\n",
    "ax.set_yticklabels(areas, color=\"white\", fontsize=12)\n",
    "\n",
    "# 去掉 x 轴刻度和所有边框\n",
    "ax.set_xticks([])\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_visible(False)\n",
    "\n",
    "# 主标题（居中）\n",
    "fig.text(0.5, 0.92, \"2022年上半年各区域对比去年销量\",\n",
    "         ha=\"center\", va=\"center\", fontsize=22, color=\"white\")\n",
    "\n",
    "# 副标题（稍偏右）\n",
    "fig.text(0.5, 0.86,\n",
    "         \"2022年整体销量高于2021年，只有东北区域较2021有所下降\",\n",
    "         ha=\"center\", va=\"center\", fontsize=11, color=\"white\")\n",
    "\n",
    "# 顶部年份标签「2022」「2021」\n",
    "fig.text(0.18, 0.80, \"2022\", color=\"#00A9FF\", fontsize=14, ha=\"center\")\n",
    "fig.text(0.82, 0.80, \"2021\", color=\"#FF7A99\", fontsize=14, ha=\"center\")\n",
    "\n",
    "# 在条形末端标注数值\n",
    "for i, (v22, v21) in enumerate(zip(values_2022, values_2021)):\n",
    "    # 左侧 2022 数值，靠近条形右端（注意 v22 是负数，所以取绝对值）\n",
    "    ax.text(v22 + max_val * 0.02, y_pos[i],\n",
    "            f\"{abs(v22)}\", va=\"center\", ha=\"right\",\n",
    "            color=\"white\", fontsize=11)\n",
    "    # 右侧 2021 数值\n",
    "    ax.text(v21 - max_val * 0.02, y_pos[i],\n",
    "            f\"{v21}\", va=\"center\", ha=\"left\",\n",
    "            color=\"white\", fontsize=11)\n",
    "\n",
    "# 底部注释\n",
    "fig.text(0.02, 0.03,\n",
    "         \"*注：数据来源于公司销售系统，统计日期截至2022.06.30\",\n",
    "         ha=\"left\", va=\"center\", fontsize=9, color=\"white\")\n",
    "\n",
    "# 微调布局，给标题和脚注留出空间\n",
    "plt.tight_layout(rect=(0.02, 0.08, 0.98, 0.85))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "879882ad-c04a-4718-bf98-f8746891302a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
